/Tree-based-method-comparison

Application and comparison of thee-based methods on Boston Housing data

Primary LanguagePython

Tree-based method comparison

Introduction

In this project, we explore and compare tree-based methods (CART, bagging, random forest, and boosting) in terms of performance and interpretability of results on the benchmark Boston Housing dataset.

Dependencies

The project made use of the scikit-learn, matplotlib, and seaborn libraries. Before proceeding be sure you have installed the necessary dependencies running the command: pip3 install -r requirements.txt

Project structure

The project is divided into:

  • figures: containing the figures generated by the analysis.
  • src: containing the source code for classes, methods and functions
    • AutoTree.py: containing the class created to manage decision trees and methods.
    • AutoEnsemble.py: containing the class created to manage decision tree ensemble and methods.
    • utils.py: containing the support function used in the analysis.
  • BostonHousing.ipynb: containing the notebook for the analysis (basically you can clone the repo, and run this notebook to see the results yourself)
  • requirements.txt: self-explicative.
  • Boston.csv: data used for the comparison.
  • main.py

Usage

To run the project:

  • From Notebook: modify the path variable in the second cell of the notebook with the path of the Boston.csv file and run all cells.
  • From main.py: modify the path variable in the main.py file with the path of the Boston.csv file and run the file.

Next steps

  • Translate the report in english